Revision

My interactive table was grouped, when it should have been ungrouped to allow for more searching.

Setup

library(tidyverse)
library(gt)
library(kableExtra)
library(DT)
names_a <- read_csv(here::here("Week9", "Lab", "StateNames_A.csv"))
names_a <- names_a |>
  rename(Sex = Gender)

3. Summarizing & Visualizing the Number of Allisons

1.

allison_data <- names_a |>
  filter(Name == "Allison") |>
  select(-Year) |>
  group_by(State, Sex) |>
  summarize(n = sum(Count)) |>
  pivot_wider(names_from = Sex,
              values_from = n,
              values_fill = 0) |>
  rename(Male = M, Female = F)

allison_data |>
  head(n = 15) |>
  knitr::kable(
    caption = "Number of Babies Named \"Allison\" by State and Sex (head=15)"
  ) |>
  kable_styling(font_size = 15)

Number of Babies Named "Allison" by State and Sex (head=15)
State	Female	Male
AK	232	0
AL	1535	0
AR	1198	0
AZ	1880	0
CA	12413	0
CO	1594	0
CT	1099	0
DC	321	0
DE	294	0
FL	4455	0
GA	3257	0
HI	183	0
IA	1477	0
ID	451	0
IL	5110	0

DT::datatable(names_a)

2.

allison_f <- names_a |>
  filter(Name == "Allison", 
         Sex == "F") |>
  select(-State) |>
  group_by(Year) |>
  summarize(n = sum(Count))

allison_f |>
  head(n = 15) |>
  knitr::kable(
    caption = "Number of Babies Named \"Allison\" with Female Sex by Year (head=15)"
  ) |>
  kable_styling(font_size = 15)

Number of Babies Named "Allison" with Female Sex by Year (head=15)
Year	n
1997	7274
1998	7861
1999	7023
2000	6314
2001	6209
2002	6237
2003	5850
2004	5871
2005	5631
2006	5560
2007	5450
2008	6237
2009	6579
2010	5856
2011	5453

3.

allison_f |>
  ggplot(mapping = aes(x = Year, y = n)) +
  geom_line() +
  geom_point() +
  labs(y = NULL,
       title = "Number of Babies With The Name Allison By Year"
       )

4. Modeling the Number of Allisons

Fit a linear model with the year as the explanatory variable, and the number of Allisons as the response. Similar to #3, each year should have one observation–the total number of Allisons born that year.

allison_lm <- lm(n ~ Year, data = allison_f)

Write out the estimated regression equation.

n = 209689.8 - 101*Year

Plot the residuals of the model, that is, the actual values minus the predicted values. Comment on the residuals - do you see any patterns?

allison_lm |>
  broom::augment() |>
  ggplot(mapping = aes(y = .resid, x = .fitted)) +
  geom_point() +
  labs(x = "Fitted",
       y = "Residuals",
       title = "Residuals of the Allison Model"
       )

The residuals start tight and spread out over time on the y-axis, so at the end they are very spread out. They almost seem to trend downward too.

From the spread of this model, we can see the name is very varied in popularity, but does seem to be more unpopular now

5. Spelling by State

8.

alan_spelling <- names_a |>
  filter(Name %in% c("Allan", "Alan", "Allen")) |>
  group_by(Name, Year) |>
  summarize(n = sum(Count))

alan_spelling %>%
  head(n = 15) %>%
  knitr::kable(
    caption = "Number of Babies Named Different Allen Spellings by Year (head=15)",
    booktabs = TRUE
  ) %>%
  kable_styling(font_size = 10,
                latex_options = "striped",
                )

Number of Babies Named Different Allen Spellings by Year (head=15)
Name	Year	n
Alan	1997	2155
Alan	1998	2102
Alan	1999	2220
Alan	2000	2398
Alan	2001	2622
Alan	2002	2591
Alan	2003	3083
Alan	2004	3051
Alan	2005	3189
Alan	2006	3442
Alan	2007	3225
Alan	2008	3002
Alan	2009	2817
Alan	2010	2487
Alan	2011	2321

alan_spelling |>
  ggplot(mapping = aes(x = Year, y = n, color = Name)) +
  geom_line() +
  geom_point() +
  labs(
    y = NULL,
    title = "Number of Babies with Different Spellings of Allen by Year"
  )

9.

allan_state <- names_a |>
  filter(Name %in% c("Allan", "Alan", "Allen"),
         Year == 2000,
         Sex == "M",
         State %in% c("CA", "PA")) |>
  select(-Sex, -Year) |>
  group_by(Name, State) 

allan_state_wide <- allan_state |>
  pivot_wider(names_from = Name,
              values_from = Count,
              values_fill = 0)

allan_state_wide |>
  knitr::kable(
    caption = "Babies Born with Differing Spellings of Alan by State"
  )

Babies Born with Differing Spellings of Alan by State
State	Alan	Allen	Allan
CA	579	176	131
PA	51	56	12

allan_percent <- allan_state |>
  group_by(State) |>
  mutate(prop = Count / sum(Count)) |>
  select(-Count) |>
  pivot_wider(names_from = Name,
              values_from = prop,
              values_fill = 0)

# Why is it moving the state column??????? 
allan_percent %>%
  gt() %>%
  fmt_percent(columns = Alan:Allan, decimals = 2)

Alan	Allen	Allan
CA
65.35%	19.86%	14.79%
PA
42.86%	47.06%	10.08%